AI Engineer

• Configure and customize agentic tools — including GitHub Copilot (agent and plan modes) — to fit the squad's specific context, codebase, and delivery standards.
• Design and maintain integration roadmaps that define how AI capabilities plug into each stage of the SDLC, from requirements and technical solution through development, testing, code review, and deployment — without disrupting the client's existing, mature DevOps pipeline.
• Ensure output quality upstream and downstream: review, validate, and refine what AI agents produce at each stage, identifying deviations early and adjusting configurations, prompts, and guardrails to keep quality consistent and trustworthy.
• Build and refine prompt engineering assets — including specification prompts, code generation instructions, test generation templates, and documentation workflows — tailored to the squad's architecture and ways of working.
• Conduct tooling audits and support value stream mapping sessions, identifying bottlenecks in the SDLC where the right agentic capability can eliminate waiting time and reduce lead time.
• Define and implement guardrails and ethics and compliance standards for AI usage within the development workflow, ensuring responsible and governed adoption aligned with the client's policies.
• Pair daily with developers, QAs, and BAs to embed agentic practices into real work items, coaching team members on effective agent use and gradually transferring ownership as the squad gains autonomy.
• Develop and maintain technical documentation that captures agent configurations, integration patterns, and best practices, feeding both the squad's day-to-day operations and the final Agentic SDLC Playbook.
• Continuously monitor AI system performance — adoption rates, output quality, cycle time impact — and iterate on configurations to drive measurable improvements every two-week cycle.
• Support problem-solving at the intersection of AI tooling and software delivery, acting as the technical reference when agents produce unexpected results or when new SDLC stages need to be instrumented.

Senior AI Engineer

• Design, develop, test, and maintain production software services and APIs
• Integrate AI/ML capabilities into enterprise software, including LLM-based features and RAG (Retrieval-Augmented Generation) pipelines
• Build agentic AI systems at a high level, designing tool-calling workflows, orchestrating multi-step autonomous tasks, and connecting models to internal services and data sources
• Work with model APIs, prompt engineering, and evaluation of AI outputs
• Contribute to code through pair programming and code reviews
• Write automated tests (unit, integration) and technical documentation
• Collaborate using version control (Git) and CI/CD pipelines
• Participate in team discussions, architecture decisions, and contribute ideas

AI Services Developer

Machine Learning Engineer

• Identify opportunities to apply the latest advances in Machine Learning to create, test, and validate predictive models;
• Create algorithms to extract insights from large datasets;
• Compare results from various methodologies and recommend the best techniques to stakeholders;
• Design, develop, and deploy analytical solutions using open-source tools (Python, TensorFlow);
• Develop and integrate automated processes for deployment and predictive validation of AI models;
• Integrate AI models into Android smartphones

Machine Learning Analyst – Embedded Systems

• Work on the platform with a strong focus on MLOps/LLMOps, backend, and integrations.
• Build and maintain automation pipelines for continuous deployment of ML and LLM models.
• Monitor model performance, detect drift, and automate reprocessing/retraining.
• Develop and integrate APIs for communication between AI models and internal systems using FastAPI.
• Work with advanced orchestration in Kubernetes and Airflow to ensure scalability and resilience.

• Study and transform data science projects into efficient machine learning models
• Develop and improve model pipelines (development, deployment, and monitoring)
• Research and implement machine learning algorithms and tools
• Assist with and continuously improve existing notebooks/code and workflows handling large volumes of real-world data to enhance their performance
• Contribute to and validate research and selection of appropriate data for models, as well as the best methods for data representation
• Research and develop the design, development, testing, and deployment of optimization algorithms, deep learning, and robotics/AI
• Develop and validate control and monitoring of algorithms and models in production
• Conduct and validate studies and analyses to understand various aspects of data science. Knowledge of Docker.

Senior Machine Learning Engineer

• Lead and architect IDP (Intelligent Document Processing) solutions: develop, train, and optimize robust pipelines for extracting unstructured data from complex documents, defining efficient fine-tuning, RAG, and computer vision strategies.
• Architect Agentic AI development: design and productionize complex agentic workflows using LangGraph, n8n, and others, ensuring state management, resilience, low latency, and failure handling in the orchestration of the formalization and credit pipeline.
• Model structured data for new AI products: build statistical pipelines and advanced analytical models on the data extracted by IDPs, transforming raw data into strategic business reports, market indices, and predictive intelligence.
• Ensure Quality, Observability, and Cost (Evals & FinOps): structure automated evaluation frameworks (LLM Evals) to measure hallucination, accuracy, and recall of models in production, optimizing token consumption and infrastructure costs.
• Autonomy and cross-functional collaboration: work directly with Product Managers, Engineering, and Technical Leadership to translate market pain points into viable architectures, participating actively from PoC design through final production delivery.
• Mentorship and technical culture: support the technical development of more junior team members, disseminating best practices for coding, modeling, and AI usage.

• Provide technical leadership for the full lifecycle of generative AI full-stack solutions (front-end React/Node, back-end Python), applying Spec-Driven Development (specification and tests before implementation) and Harness Engineering (automated validation of the rendered artifact) as the single engineering standard — complementary, not competing approaches;
• Architect and operate RAG, LLMOps, and MLOps pipelines on Azure AI Foundry, ensuring network isolation, sandboxing for execution of code generated by LLMs, and end-to-end traceability;
• Implement governance, security, and privacy controls for AI by design — model risk management, prevention of prompt injection/data leakage, and minimization and anonymization of personal and health data in accordance with LGPD;
• Establish and maintain automated quality gates (QA harness) for AI-generated outputs, providing deterministic feedback (coordinates/metrics) instead of subjective evaluation;
• Serve as the technical reference for internal teams and vendors, defining architecture standards, code review practices, and acceptance criteria for AI solutions;

Artificial Intelligence Engineer, AI

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AI Engineer II

• Desenvolver aplicações que integram LLMs (Gemini, Claude) via Vertex AI: pipelines de RAG, function calling, orquestração de agentes e automações inteligentes.
• Ser dono(a) da orquestração de IA e da camada de aplicação — o engenheiro de dados cuida da ingestão e do vector store, você consome e orquestra.
• Subir e sustentar a solução em produção no GCP: infraestrutura como código, CI/CD, IAM e observabilidade.
• Acompanhar custo e latência dos modelos em produção, mantendo a solução eficiente.
• Implementar guardrails e avaliação de qualidade (evals) em parceria com o PM.
• Construir integrações com sistemas financeiros, RPAs e ferramentas de workflow.
• Escrever código de produção com testes, segurança e boas práticas.

Engenheiro de Software Sênior – IA Aplicada

• Develop AI agents.
• Build automation flows.
• Integrate agents with SAP S/4 HANA, Microsoft 365 and other systems.
• Develop APIs.
• Implement RAG (Retrieval-Augmented Generation).
• Create knowledge bases.
• Integrate vector databases.
• Develop tools for agents.
• Deploy agents to production.
• Monitor performance.
• Continuously evolve the agents.

AI Agent Developer

• Lead a multidisciplinary squad, fostering a collaborative, autonomous, and results-oriented environment.
• Orchestrate the use of AI agents across the entire Software Development Life Cycle (SDLC), ensuring their application in appropriate contexts and adherence to quality and governance standards.
• Facilitate Agile ceremonies (Planning, Daily, Review, and Retrospective), adapting the workflow to a development model supported by AI.
• Manage the backlog in partnership with Product Managers and stakeholders, prioritizing deliveries that provide the greatest business value.
• Continuously monitor the quality of human deliverables and outputs generated by AI agents, refining workflows, prompts, and configurations as needed.
• Define and track productivity, quality, efficiency, and AI adoption metrics for the squad.
• Remove impediments and manage risks, ensuring predictability in deliveries.
• Promote continuous alignment between product, business, and technology, ensuring transparent communication with all stakeholders.
• Encourage technical development of the team and continuous improvement of engineering practices and collaboration with AI.
• Identify opportunities for continuous improvement, automation, and optimization of the development process.

AI Squad Lead – AI Orchestration, Software Delivery

• Lead, guide, and mentor the team, fostering professional growth and technical autonomy.
• Conduct and actively participate in code reviews, ensuring quality, scalability, and maintainability of solutions.
• Define and promote development and architecture best practices.
• Serve as a technical escalation point for the team, assisting in resolving complex issues and bottlenecks.
• Collaborate with management to set priorities, plan the technical roadmap, and manage the team backlog.
• Design and architect robust, scalable AI systems for production deployment.
• Ensure the performance and stability of AI agents in production through continuous monitoring and optimization.
• Explore and test new technologies, frameworks, and AI models to keep the company at the forefront of innovation.
• Document architectures, processes, and technical decisions clearly and accessibly.
• Work closely with Product, Engineering, and business teams to understand challenges and translate them into viable AI solutions.
• Effectively communicate progress, challenges, and outcomes of AI projects to technical and non-technical stakeholders.
• Contribute to the long-term vision for AI, identifying opportunities to leverage artificial intelligence to drive business growth.

Tech Lead, AI

• Conduct organizational diagnostics, redesign workflows and operating models, support technology adoption, and use AI tools to demonstrate value inside real client processes
• Map current-state workflows end-to-end and diagnose root causes of operational friction
• Redesign processes for target-state operations
• Establish performance baselines before engagements begin; build the measurement framework that tracks improvement over the engagement
• Identify value gaps in stalled implementations
• Build reporting cadences that give clients a clear line from initiative to impact
• Build working AI-powered prototypes directly
• Use prototypes as the primary tool for stakeholder alignment
• Evaluate where AI changes a workflow and where it is a distraction
• Support rollout of new tools and workflows; ensure adoption is grounded in process design and change management
• Design enablement materials that make new technology stick;
• Support complex engagement scoping where client needs are ambiguous or span multiple services
• Conduct diagnostic assessments

Forward Deployed Engineer, AI and Business Transformation

• Develop and deploy Generative Artificial Intelligence systems and multi-agent AI agents (stateful and autonomous).
• Design and optimize RAG (Retrieval-Augmented Generation) architectures using vector databases to build intelligent knowledge bases.
• Develop and test advanced prompt engineering strategies, few-shot prompting techniques, and fine-tuning approaches for foundation models.
• Collaborate on building robust connectors and APIs to integrate AI models with enterprise systems such as CRMs, ERPs, and e-commerce platforms.
• Ensure governance, security, reduction of hallucinations, and cost optimization for LLM API calls.

• Develop and deploy Artificial Intelligence applications, including AI agents, meeting functional and non-functional requirements;
• Develop clean, testable, and scalable code for Machine Learning applications;
• Create scalable inference endpoints to serve models;
• Implement MLOps practices throughout the model lifecycle;
• Work with experiment tracking, model registry, and model versioning;
• Contribute to CI/CD pipelines for data and models;
• Implement automated testing and rollout/rollback strategies for Machine Learning models;
• Collaborate with the team on the development and evolution of AI solutions.

Machine Learning Engineer – Senior

• Design and execute advanced AI/ML models – including fine-tuned LLMs and RAG architectures – for integration into both internal tooling and external client-facing products.
• Lead experimentation efforts: clearly define hypotheses, design interpretable tests, analyze results rigorously, and communicate findings effectively.
• Serve as the technical SME for GenAI/LLM-powered tools, from foundational architecture to model optimization and deployment.
• Work with software and data engineering to productionize models and contribute to robust CI/CD pipelines with MLOps/LLMOps best practices.
• Stay current on emerging methods in AI – e.g., agentic workflows, memory-augmented systems, multimodal embeddings – and bring those ideas to life in real-world applications.
• Promote AI adoption internally by educating and empowering cross-functional partners while improving processes and workflows.
• Manage your time and projects effectively, ensuring clear progress updates and proactive communication with collaborators.
• Write production-grade Python code to support models, pipelines, and experimental frameworks.
• Work with the highest degree of ethics and empathy, recognizing that AI can be a potentially disruptive force for organizations and individuals.

Senior AI/ML Engineer – Technology

• Lead the MLOps strategy on Databricks, defining architecture and standards;
• Design and review CI/CD pipelines for ML models (training, packaging, deployment, rollback);
• Define practices for model versioning and registry, feature stores, experiment reproducibility, and serving;
• Establish end-to-end observability: performance, data/model drift, data quality, and business metrics;
• Review PRs and critical architecture decisions; mentor the squad on the most complex topics;
• Ensure governance, security, and cost control of the data and AI platform;
• Participate in key ceremonies (planning, technical refinements, checkpoints), optimized for a part-time workload.

Tech Lead, MLOps – Senior

• Design and maintain CI/CD pipelines for ML models and LLM/RAG applications (training, packaging, deployment, and rollback);
• Operationalize the model lifecycle: versioning, model registry, feature stores, orchestration, and experiment reproducibility;
• Build and scale serving platforms (batch and online) with focus on latency, cost, and availability;
• Implement end-to-end observability: performance monitoring, data/model drift detection, data quality, and business metrics;
• Establish governance, security, and cost-control practices for data and AI infrastructure;
• Collaborate with data science and engineering teams to standardize environments and reduce time from lab to production;
• Define standards, documentation, and MLOps/LLMOps best practices for squads.

Senior MLOps Engineer

• Conectar talentos de tecnologia a projetos reais de alto impacto
• Oferecer consultoria tech e squads de alta performance
• Construir soluções de IA de forma autônoma
• Propor soluções técnicas inovadoras e avaliar tecnologias
• Trabalhar em colaboração com equipes diversas e líderes do cliente

Engenheiro de Inteligência Artificial e Machine Learning – Sênior

• Connect technology talent to real, high-impact projects in healthy environments, with end-to-end professional growth opportunities.
• Collaborate with high-performance teams on challenging projects.

Machine Learning Engineer jobs in Brazil

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Senior AI Engineer

AI Services Developer

Machine Learning Analyst – Embedded Systems

Senior AI Engineer

Senior Machine Learning Engineer

Machine Learning Engineer

Artificial Intelligence Engineer, AI

AI Engineer II

Engenheiro de Software Sênior – IA Aplicada

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AI Squad Lead – AI Orchestration, Software Delivery

Tech Lead, AI

Forward Deployed Engineer, AI and Business Transformation

AI Engineer

Machine Learning Engineer – Senior

Senior AI/ML Engineer – Technology

Tech Lead, MLOps – Senior

Senior MLOps Engineer

Engenheiro de Inteligência Artificial e Machine Learning – Sênior

Artificial Intelligence and Machine Learning Engineer – Mid-level